92 lines
3.7 KiB
Python
92 lines
3.7 KiB
Python
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# OtterTune - test_cluster.py
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#
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# Copyright (c) 2017-18, Carnegie Mellon University Database Group
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#
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import unittest
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import numpy as np
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from sklearn import datasets
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from analysis.cluster import KMeans, KMeansClusters, create_kselection_model
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class TestKMeans(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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super(TestKMeans, cls).setUpClass()
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iris = datasets.load_iris()
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cls.model = KMeans()
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cls.model.fit(iris.data, 5, iris.target,
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estimator_params={'n_init': 50, 'random_state': 42})
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def test_kmeans_n_clusters(self):
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self.assertEqual(self.model.n_clusters_, 5)
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def test_kmeans_cluster_inertia(self):
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self.assertAlmostEqual(self.model.cluster_inertia_, 46.535, 2)
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def test_kmeans_cluster_labels(self):
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expected_labels = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 2, 3, 3, 3,
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2, 3, 2, 2, 3, 2, 3, 2, 3, 3, 2, 3, 2, 3, 2, 3, 3, 3, 3,
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3, 3, 3, 2, 2, 2, 2, 3, 2, 3, 3, 3, 2, 2, 2, 3, 2, 2, 2,
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2, 2, 3, 2, 2, 4, 3, 0, 4, 4, 0, 2, 0, 4, 0, 4, 4, 4, 3,
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4, 4, 4, 0, 0, 3, 4, 3, 0, 3, 4, 0, 3, 3, 4, 0, 0, 0, 4,
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3, 3, 0, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 3]
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for lab_actual, lab_expected in zip(self.model.cluster_labels_, expected_labels):
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self.assertEqual(lab_actual, lab_expected)
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def test_kmeans_sample_labels(self):
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for lab_actual, lab_expected in zip(self.model.sample_labels_, datasets.load_iris().target):
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self.assertEqual(lab_actual, lab_expected)
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def test_kmeans_cluster_centers(self):
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expected_centers = [[7.475, 3.125, 6.300, 2.050],
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[5.006, 3.418, 1.464, 0.244],
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[5.508, 2.600, 3.908, 1.204],
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[6.207, 2.853, 4.746, 1.564],
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[6.529, 3.058, 5.508, 2.162]]
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for row_actual, row_expected in zip(self.model.cluster_centers_, expected_centers):
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for val_actual, val_expected in zip(row_actual, row_expected):
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self.assertAlmostEqual(val_actual, val_expected, 2)
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class TestKSelection(unittest.TestCase):
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def setUp(self):
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np.random.seed(seed=42)
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@classmethod
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def setUpClass(cls):
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super(TestKSelection, cls).setUpClass()
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# Load Iris data
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iris = datasets.load_iris()
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cls.matrix = iris.data
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cls.kmeans_models = KMeansClusters()
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cls.kmeans_models.fit(cls.matrix,
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min_cluster=1,
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max_cluster=10,
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sample_labels=iris.target,
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estimator_params={'n_init': 50, 'random_state': 42})
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def test_detk_optimal_num_clusters(self):
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# Compute optimal # cluster using det-k
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detk = create_kselection_model("det-k")
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detk.fit(self.matrix, self.kmeans_models.cluster_map_)
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self.assertEqual(detk.optimal_num_clusters_, 2)
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def test_gap_statistic_optimal_num_clusters(self):
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# Compute optimal # cluster using gap-statistics
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gap = create_kselection_model("gap-statistic")
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gap.fit(self.matrix, self.kmeans_models.cluster_map_)
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self.assertEqual(gap.optimal_num_clusters_, 8)
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def test_silhouette_optimal_num_clusters(self):
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# Compute optimal # cluster using Silhouette Analysis
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sil = create_kselection_model("s-score")
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sil.fit(self.matrix, self.kmeans_models.cluster_map_)
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self.assertEqual(sil.optimal_num_clusters_, 2)
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